AUTOMATIC ROI SELECTION FOR MULTI-MODAL MEDICAL IMAGES
The Region of Interests (RoIs) in Medical Images (MIs) contain specialized form of clinical and biological data important for medical procedures and practices. Their detection with higher accuracy and representation with more efficiency have become two important requirements of many region-based...
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| Natura: | Tesi |
| Lingua: | inglese |
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DEPARTMENT OF ELECTRICAL, ELECTRONIC AND COMMUNICATION ENGINEERING
2021
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| Accesso online: | http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/619 |
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| _version_ | 1868226982031917056 |
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| author | Mohammad Abid Rahman, Chowdhury |
| author_browse | Mohammad Abid Rahman, Chowdhury |
| author_facet | Mohammad Abid Rahman, Chowdhury |
| author_sort | Mohammad Abid Rahman, Chowdhury |
| collection | DSpace |
| description | The Region of Interests (RoIs) in Medical Images (MIs) contain specialized form
of clinical and biological data important for medical procedures and practices. Their
detection with higher accuracy and representation with more efficiency have become
two important requirements of many region-based MI processing. However, these requirements are largely overlooked in contemporary literature. The development of
an efficient and automatic RoI selection method has, therefore, been investigated in
this thesis to attain two major requirements: (i) faster and accurate RoI segmentation and (ii) efficient representation of the segmented RoI. An Active Contour Model
(ACM) based segmentation scheme is developed to effectively tackle intensity inhomogeneities and different shapes, sizes and locations of RoIs, and thereby, to automatically segment the RoIs with higher accuracy. Unlike the existing ACMs, two novel
local images are constructed and fitted in the relative entropy based energy functional
for better curve evolution and increasing robustness to noise and initialization. The
energy equation is also scaled by local dispersion based edge mapped image for accuracy in boundary detection and smoothness. For the efficient representation of the
segmented RoIs, the segmented region is defined by its original shape with reduced
information by an effective polygonal decimation process. The overall performance
of the proposed automatic RoI selection method is verified with the effectiveness and
efficiency of the newly developed RoI segmentation and representation schemes for
multi-modality MIs. Particularly, compared to the prominent and recent ACMs, the
proposed segmentation scheme offers a faster curve evolution and more robustness to
noisy, low-contrast, and intensity inhomogeneous MIs. The effective representation
scheme is also compared with the 5 times and 10 times reduced vertices and respective bit requirements. The utilization of the proposed RoI selection method would be
promising for the region-based MI processing and its security protection applications. |
| format | Thesis |
| id | oai:localhost:123456789-619 |
| institution | My University |
| language | English |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | DEPARTMENT OF ELECTRICAL, ELECTRONIC AND COMMUNICATION ENGINEERING |
| publisherStr | DEPARTMENT OF ELECTRICAL, ELECTRONIC AND COMMUNICATION ENGINEERING |
| record_format | dspace |
| spelling | oai:localhost:123456789-6192021-09-30T05:48:12Z AUTOMATIC ROI SELECTION FOR MULTI-MODAL MEDICAL IMAGES Mohammad Abid Rahman, Chowdhury The Region of Interests (RoIs) in Medical Images (MIs) contain specialized form of clinical and biological data important for medical procedures and practices. Their detection with higher accuracy and representation with more efficiency have become two important requirements of many region-based MI processing. However, these requirements are largely overlooked in contemporary literature. The development of an efficient and automatic RoI selection method has, therefore, been investigated in this thesis to attain two major requirements: (i) faster and accurate RoI segmentation and (ii) efficient representation of the segmented RoI. An Active Contour Model (ACM) based segmentation scheme is developed to effectively tackle intensity inhomogeneities and different shapes, sizes and locations of RoIs, and thereby, to automatically segment the RoIs with higher accuracy. Unlike the existing ACMs, two novel local images are constructed and fitted in the relative entropy based energy functional for better curve evolution and increasing robustness to noise and initialization. The energy equation is also scaled by local dispersion based edge mapped image for accuracy in boundary detection and smoothness. For the efficient representation of the segmented RoIs, the segmented region is defined by its original shape with reduced information by an effective polygonal decimation process. The overall performance of the proposed automatic RoI selection method is verified with the effectiveness and efficiency of the newly developed RoI segmentation and representation schemes for multi-modality MIs. Particularly, compared to the prominent and recent ACMs, the proposed segmentation scheme offers a faster curve evolution and more robustness to noisy, low-contrast, and intensity inhomogeneous MIs. The effective representation scheme is also compared with the 5 times and 10 times reduced vertices and respective bit requirements. The utilization of the proposed RoI selection method would be promising for the region-based MI processing and its security protection applications. 2021-09-30T05:48:06Z 2021-09-30T05:48:06Z 2020-07 Thesis http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/619 en application/pdf DEPARTMENT OF ELECTRICAL, ELECTRONIC AND COMMUNICATION ENGINEERING |
| spellingShingle | Mohammad Abid Rahman, Chowdhury AUTOMATIC ROI SELECTION FOR MULTI-MODAL MEDICAL IMAGES |
| title | AUTOMATIC ROI SELECTION FOR MULTI-MODAL MEDICAL IMAGES |
| title_full | AUTOMATIC ROI SELECTION FOR MULTI-MODAL MEDICAL IMAGES |
| title_fullStr | AUTOMATIC ROI SELECTION FOR MULTI-MODAL MEDICAL IMAGES |
| title_full_unstemmed | AUTOMATIC ROI SELECTION FOR MULTI-MODAL MEDICAL IMAGES |
| title_short | AUTOMATIC ROI SELECTION FOR MULTI-MODAL MEDICAL IMAGES |
| title_sort | automatic roi selection for multi modal medical images |
| url | http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/619 |
| work_keys_str_mv | AT mohammadabidrahmanchowdhury automaticroiselectionformultimodalmedicalimages |